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Matching avoids making assumptions about the
functional form of the regression equation, making analysis more
reliable

Elevator pitch

“Matching” is a statistical technique used to
evaluate the effect of a treatment by comparing the treated and non-treated
units in an observational study. Matching provides an alternative to older
estimation methods, such as ordinary least squares (OLS), which involves
strong assumptions that are usually without much justification from economic
theory. While the use of simple OLS models may have been appropriate in the
early days of computing during the 1970s and 1980s, the remarkable increase
in computing power since then has made other methods, in particular
matching, very easy to implement.

Key findings

Pros

Matching allows for the estimation
of causal effects without relying on such strong assumptions,
which makes its results more reliable.

Matching allows the researcher to
balance two problems that plague statistical estimation: bias
and variance.

The potential lack of similar
individuals in treatment and comparison groups is highlighted by
matching.

Cons

Matching can be computationally
intensive.

Both matching and OLS still rely on
strong assumptions about the exogeneity of the treatment, which
makes results less reliable.

Matching requires decisions at
several steps of the process that may bias the estimates and
limit their precision.

Author's main message

Matching is a powerful but often misunderstood
statistical technique. It allows the researcher to program impacts (in a
similar way to regression analysis) but does so without requiring
researchers to make assumptions about the exact functional form. This can
avoid the potential for some very serious errors occurring regarding the
predicted impacts of programs—which makes matching an important component of
the statistical toolbox for policymakers.